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E. Nemer, R. Goubran, and S. Mahmoud, “Robust voice activity detection using higher-order statistics in the LPC residual domain [J],” IEEE Transactions on Speech and Audio Processing, Vol. 9, No. 3, pp. 217–231, 2001.

has been cited by the following article:

• TITLE: Robust Speech Endpoint Detection in Airplane Cockpit Voice Background

  AUTHORS: Hongbing CHENG, Ming LEI, Guorong HUANG, Yan XIA

  KEYWORDS: Complex Laplacian Model, Maximum A Posterior Criterion, Likelihood Ratio Test, Speech End- point Detection, Airplane Cockpit Voice

  JOURNAL NAME: Wireless Sensor Network, Vol.1 No.5, December 16, 2009

  ABSTRACT: A method of robust speech endpoint detection in airplane cockpit voice background is presented. Based on the analysis of background noise character, a complex Laplacian distribution model directly aiming at noisy speech is established. Then the likelihood ratio test based on binary hypothesis test is carried out. The decision criterion of conventional maximum a posterior incorporating the inter-frame correlation leads to two separate thresholds. Speech endpoint detection decision is finally made depend on the previous frame and the observed spectrum, and the speech endpoint is searched based on the decision. Compared with the typical algorithms, the proposed method operates robust in the airplane cockpit voice background.